New Framework for Simultaneous Localization and Mapping Multi Map SLAM

Publisher:
IEEE
Publication Type:
Conference Proceeding
Citation:
Proceeding of 2008 IEEE International Conference on Robotics and Automation, 2008, pp. 1892 - 1897
Issue Date:
2008-01
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The main contribution of this paper arise from the development of a new framework for the problem of Simultaneous Localization and Mapping (SLAM) in the domain of stereo vision based robot navigation. The new framework has its inspiration in the mechanics of human navigation. At present the solution is specific to a unique instance of SLAM, where the primary sensing device is a short baseline stereo vision system. The new framework addresses several key issues of this particular problem. As observed in our earlier work [1], the particular sensing device has a highly nonlinear observation model resulting in inconsistent state estimations when standard recursive estimators such as the Extended Kalman Filter (EKF) or the Unscented variants are used. Secondly, vision based approaches tend to have issues related to large feature density, narrow field of view and the potential requirement of maintaining large data bases for vision based data association techniques. The proposed Multi Map SLAM solution addresses the first issue by formulating the SLAM problem as a nonlinear batch optimization. Second issue is addressed through a two tier map representation. The two maps have unique attributes assigned to them. The Global Map (GM) is a compact global representation of the robots environment and the Local Map (LM) is exclusively used for low-level navigation between local points in the robots navigation horizon.
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